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SEASONAL FECUNDITY OF SAGEBRUSH BREWER'S SPARROW (SPIZELLA BREWERI BREWERI) AT THE NORTHERN EDGE OF ITS BREEDING RANGE

2006· article· en· W2150384628 on OpenAlexaff
Nancy A. Mahony, Pamela G. Krannitz, Kathy Martin

Bibliographic record

VenueThe Auk · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFecundityEcologyBiologyNest (protein structural motif)ProductivityRange (aeronautics)PredationSparrowGeographyPopulationDemography

Abstract

fetched live from OpenAlex

We examined the effects of environmental and ecological factors associated with seasonal fecundity on spatial and temporal patterns of productivity in Sagebrush Brewer's Sparrow (Spizella breweri breweri) in British Columbia, the northwestern edge of its breeding range. This study is the first examination of seasonal fecundity in this species. Seasonal fecundity of females varied from 1998 to 2000. It was highest in 2000, when nest predation was lowest and number of clutches per female was intermediate, and lowest in 1998, when nest predation and number of clutches per female were the highest and warm El Niño conditions led to early breeding. Potential fecundity gains from early breeding were diminished by the interaction of shifting predation rates and variable effects of weather at different elevations. Early breeding in 1998 proved an advantage only at the low-elevation site, because an early spring storm destroyed 43% and 20% of first nests at two high-elevation sites. High seasonal fecundity varied between the sites, such that the best site in 1998 became the least productive in 1999 and vice versa. The overriding factor driving spatiotemporal variation was shifting rates of nest predation, though the elevation-related storm effects and variation in number of clutches were partly responsible. To maintain high productivity for Sagebrush Brewer's Sparrow at the northern edge of its range, where conditions are unpredictable and where there is no consistently best or worst site in terms of productivity, managers must protect sites from habitat loss or alteration across a range of elevations and conditions. Fécondité Saisonnière de Spizella Breweri Breweri à la Limite Nord de Son Aire de Reproduction

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.203
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2006
Admission routes1
Has abstractyes

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